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CybersecurityDeployed

FakeGuard AI

Client

Enterprise Security

Timeline

4 Weeks

Platform

API & Web App

Team

6 Engineers

Project Overview

The Problem: Financial institutions needed a way to verify KYC video uploads against emerging deepfake technologies.

The Need: A high-accuracy, low-latency API capable of analyzing video frames for AI manipulation artifacts.

Why it Mattered: Preventing millions of dollars in identity fraud.

Core Goals

  • Detect deepfakes
  • Real-time API
  • Enterprise compliance

Business Problems

Sophisticated Fraud

Standard biometric systems were failing against high-quality deepfakes.

Technical Challenges

Processing Power

Running heavy ML video models quickly enough for a seamless user experience.

False Positives

Balancing strict security with low false-rejection rates.

Research & Discovery

1

Model Training

Trained custom CNNs and Vision Transformers on vast datasets of manipulated media.

Our Solution

We architected a custom ecosystem broken down into specific operational modules.

Analysis Engine

Distributed GPU pipeline for rapid video processing.

Enterprise API

Secure, rate-limited REST API for banking integrations.

Analytics Portal

Dashboard showing fraud attempt hotspots and threat intelligence.

Development Process

Discovery & Requirements

Milestone 1

UI/UX Design Prototyping

Milestone 2

Database Architecture

Milestone 3

Backend API Development

Milestone 4

Frontend Implementation

Milestone 5

QA & Load Testing

Milestone 6

Deployment & Training

Milestone 7

System Architecture

User Devices
CDN / Load Balancer
Auth API
Core Services
Primary Database & Cache

Technology Stack

Frontend

Next.jsTailwind CSS

Backend

FastAPICeleryRedis

Database

PostgreSQLMongoDB

Cloud & DevOps

AWS SageMakerEC2 GPU Instances

Core Features Delivered

Video Deepfake Detection
Audio Spoof Detection
REST API
Webhook Alerts
Detailed Threat Reports
RBAC

Interface Showcase

Desktop UI View
Desktop UI
Mobile View
Mobile UI

Performance Metrics

98.7%

Accuracy

<2s

API Latency

$2M+

Fraud Prevented

Project Statistics

100k/day

Videos Scanned

12

GPU Nodes

99.99%

Uptime

Business Impact

Prevented over $2M in potential fraudulent account openings in Q1.

Integrated smoothly into 3 major regional banking applications.

Reduced manual video KYC review times by 85%.

Transformation

Before Jeevix

  • ✗Vulnerable to deepfakes
  • ✗Manual video review
  • ✗High fraud risk

After Jeevix

  • ✓Automated AI detection
  • ✓Instant verification
  • ✓Secured KYC pipeline

"FakeGuard AI is an absolute necessity in today's threat landscape. The API integration was flawless."

Client

CISO Placeholder

Bank Placeholder

Placeholder Client

Frequently Asked Questions

Development timelines vary based on complexity, but most of our enterprise platforms are delivered within 3 to 6 months from discovery to deployment.

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